Abnormal crowd behavior detection using behavior entropy model

Using Behavior Entropy model, we introduce a novel method to detect and localize abnormal behaviors in crowd scenes. Our key insight is to estimate the behavior entropy of each pixel and whole scene by considering defined pixels' behavior certainty. For this purpose, we introduce information theory and energetics concept to define pixel's behavior certainty based on video's spatial-temporal information. Scene entropy behavior and behavior entropy image can be used to detect and localize anomalies respectively. We discuss parameters' setting by analyzing how they influence model's detecting and localizing abilities, and our model is robust to parameter setting. The experiments are conducted on several publicly available datasets, and show that the proposed method captures the dynamics of the crowd behavior successfully. The results of our method, indicates that the method outperforms the state-of-the-art methods in detecting and localizing several kinds of abnormal behaviors in the crowd.